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Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection

Authors : Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Zhiwei Han, Martin Kleinsteuber

Published in: Machine Learning and Knowledge Discovery in Databases. Research Track

Publisher: Springer International Publishing

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Abstract

In graph analysis community detection and node representation learning are two highly correlated tasks. In this work, we propose an efficient generative model called J-ENC for learning Joint Embedding for Node representation and Community detection. J-ENC learns a community-aware node representation, i.e., learning of the node embeddings are constrained in such a way that connected nodes are not only “closer” to each other but also share similar community assignments. This joint learning framework leverages community-aware node embeddings for better performance on these tasks: node classification, overlapping community detection and non-overlapping community detection. We demonstrate on several graph datasets that J-ENC effectively outperforms many competitive baselines on these tasks. Furthermore, we show that J-ENC not only has quite robust performance with varying hyperparameters but also is computationally efficient than its competitors.

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Appendix
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Metadata
Title
Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection
Authors
Rayyan Ahmad Khan
Muhammad Umer Anwaar
Omran Kaddah
Zhiwei Han
Martin Kleinsteuber
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases. Research Track

Print ISBN: 978-3-030-86519-1

Electronic ISBN: 978-3-030-86520-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86520-7_2

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